Considering the population growth rate of recent years, a doubling of the current worldwide
crop productivity is expected to be needed by 2050. Pests and diseases are a major …

This paper provides a comprehensive review of deep learning models for ischemic stroke
lesion segmentation in medical images. Ischemic stroke is a severe neurological disease …

Today's deep learning methods focus on how to design the objective functions to make the
prediction as close as possible to the target. Meanwhile, an appropriate neural network …

Change detection (CD) aims to identify surface changes from bitemporal images. In recent
years, deep learning (DL)-based methods have made substantial breakthroughs in the field …

Under ideal environmental conditions, RGB-based deep convolutional neural networks can
achieve high performance for salient object detection (SOD). In scenes with cluttered …

Deep learning has given way to a new era of machine learning, apart from computer vision.
Convolutional neural networks have been implemented in image classification …

We present a novel technique to automatically colorize grayscale images that combines
both global priors and local image features. Based on Convolutional Neural Networks, our …

Lesion segmentation algorithms automatically outline lesion areas in medical images,
facilitating more effective identification and assessment of the clinically relevant features …

Training thin deep networks following the student-teacher learning paradigm has received
intensive attention because of its excellent performance. However, to the best of our …

Computer-aided medical image analysis plays a significant role in assisting medical
practitioners for expert clinical diagnosis and deciding the optimal treatment plan. At present …